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Facile synthesis of Mn-Ni bimetal organic framework decorated with amine as an electrode for a high-performance supercapacitor
In the recent years, the whole world is looking for better energy storage devices. Supercapacitors are among the mostpromising high-capacity energy storage devices. Manganese-based material has gained more importance among transition metals due to its cost, easy fabrication, and wide potential applications. Here, we report the synthesis of a novel MOF using metal centers, dicarboxylate ligand, and aminoterephthalic acid as a co-ligand {Mn-Ni-NH2(h2fipbb)}MOF. The synthesized material has a unique hierarchical morphology consisting of manganese and nickel connected with NH2 through h2fipbb ligand linkage, which is highly efficient for the permeation of the electrolyte and electron transfer. The {Mn-Ni-NH2(h2fipbb)}MOF shows an excellent specific capacitance of 711.60 F g?1 using 2M KOH at a current density of 1 A g?1. The two electrode system material exhibitsa high-power and energy density of 743.99Wkg?1 and 20.49 Wh kg?1, respectively. From the above results, the synthesized {Mn-Ni-NH2(h2fipbb)}MOF can be considered a promising material for electrochemical energy storageapplications. 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Supreme court dialogue classification using machine learning models
Legal classification models help lawyers identify the relevant documents required for a study. In this study, the focus is on sentence level classification. To be more precise, the work undertaken focuses on a conversation in the supreme court between the justice and other correspondents. In the study, both the nae Bayes classifier and logistic regression are used to classify conversations at the sentence level. The performance is measured with the help of the area under the curve score. The study found that the model that was trained on a specific case yielded better results than a model that was trained on a larger number of conversations. Case specificity is found to be more crucial in gaining better results from the classifier. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
Adsorptive removal of Cr (VI) using mesoporous iron-aluminum oxyhydroxide-polyvinyl alcohol self-supporting film: Kinetics, optimization studies and mechanism
Over the past decades, the disposal of heavy metals like Cr(VI) from industries had an adverse effect on the environment, thus making it a topic of particular interest. In this context, mesoporous Aluminum oxyhydroxide-polyvinyl alcohol self-supporting films were synthesized, and different transition metals (V, Fe, Co, Ni and Cu) were incorporated by an eco-friendly route, and their adsorptive capacity towards Cr (VI) was studied. The composite mesoporous film with iron, aluminum oxyhydroxide and PVA was more efficient adsorbent than other transition metal incorporated aluminum oxyhydroxide films. The surface and chemical properties of the film were confirmed by pXRD, FTIR, Raman Spectra, BET-Surface area, BJH, SEM and Optical Profilometry. Furthermore, the effect of different parameters that impact the adsorption capacity towards Cr (VI) is discussed, including adsorbent load, contact time, solution pH, temperature, and initial concentration. A detailed investigation of the film before and after the adsorption of Cr (VI) using different characterization techniques is investigated in detail. The kinetic studies and adsorption isotherms are studied, and a suitable mechanism has been proposed for Cr (VI) removal. The synthesized films possess potential advantages like cost-effectiveness, eco-friendly nature, reusability, and higher removal efficiency towards the removal of Cr (VI) from an aqueous solution. 2023 Elsevier Ltd -
Gravity-modulated RayleighBard convection in a Newtonian liquid bounded by rigidfree boundaries: a comparative study with other boundary conditions
Effect of different boundaries on the gravity-modulated RayleighBard convection has been investigated with an emphasis on rigidfree boundaries. Small-amplitude and large-amplitude modulations are studied using the linear stability analysis. The modified Venezian approach is used to study small-amplitude modulations using different modes of perturbations and the superposition principle. The existence of subharmonic motions for the case of large-amplitude modulations was explored using the Mathieu equation arising from the linear stability analysis. Floquet theory was used together with Hills infinite determinant method to compute the critical Rayleigh number for the case of large-amplitude modulations. Weakly non-linear analysis is performed leading to the cubic StuartLandau equation from the Lorenz system. Heat transport was quantified using the Nusselt number and the mean Nusselt numbers for different amplitudes and frequencies. It was found that gravity modulation has, in general, a stabilizing effect on the convection process in all three boundary types, and the heat transport was found to be an increasing function of amplitude. Another important outcome of the study is that the critical Rayleigh number for the onset of convection for rigidfree boundaries lies between those of the corresponding values of the freefree and rigidrigid boundaries in the case of both harmonic and subharmonic motions which could be exploited in controlling convection. 2023, The Author(s), under exclusive licence to Springer Nature B.V. -
A concise route to fused tetrazolo scaffolds through 10-camphor sulfonic acid auto-tandem homogeneous catalysis and mechanistic investigation
10-Camphor sulfonic acid (10-CSA) as an organo-catalyst has gained interest due to its versatile solubility and easiness of handling. This work reports a simple synthetic method through non-classical Biginelli for the construction of tetrazolo pyrimidine (4a-m) and quinazolines (4a?-o?). Azolopyrimidines and quinazolines are of great pharmaceutical importance. Numerous compounds are currently in use for the treatment of different diseases. Therefore their synthesis is industrially inevitable. Employing aldehydes, 1,3-dicarbonyls, and 5-Aminotetrazole, we report eco-friendly, cost-effective catalysis through a tandem reaction catalyzed by the 10-CSA that gave excellent yields, 7095 % for tetrazolo quinazoline and 4576 % tetrazolo pyrimidines respectively. The homonuclear NOESY analysis confirms the selective formation of one isomer. All the compounds are characterised by 1H NMR, 13C NMR, and MS. Investigation of the reaction mechanism by both experimental and theoretical studies provides evidence. Mechanism of the reaction was also explained utilizing the information from mass spectrometry monitoring. DFT calculation carried out at PBEPBE (Perdew-Burke-Ernzerhof) functional and 6-31G (d,p) basis set level of theory of the various intermediates observed supports the experimental evidence. 2023 Elsevier B.V. -
A brief review of portfolio optimization techniques
Portfolio optimization has always been a challenging proposition in finance and management. Portfolio optimization facilitates in selection of portfolios in a volatile market situation. In this paper, different classical, statistical and intelligent approaches employed for portfolio optimization and management are reviewed. A brief study is performed to understand why portfolio is important for any organization and how recent advances in machine learning and artificial intelligence can help portfolio managers to take right decisions regarding allotment of portfolios. A comparative study of different techniques, first of its kind, is presented in this paper. An effort is also made to compile classical, intelligent, and quantum-inspired techniques that can be employed in portfolio optimization. 2022, The Author(s), under exclusive licence to Springer Nature B.V. -
The role of classroom engagement on academic grit, intolerance to uncertainty and well-being among school students during the second wave of the COVID-19 pandemic in India
The forced changes and disruptions in educational systems and learning experiences due to the pandemic has impacted students' mental health and well-being. The present study aims to understand the effects of the determinants of well-being on students in India during the second wave (April to August 2021) of the COVID-19 pandemic. The determinants of well-being in this study areacademic grit, intolerance to uncertainty and students' engagement in an online learning environment. In this study, well-being is characterized as students' confidence and satisfaction in an online learning and pandemic environment. The data collected from 1174 students (1219 years) from various states, using standardized tools, were analyzed to find out about the mediating effect of students' engagement on the relationship between academic grit and well-being, and between intolerance to uncertainty and well-being. Further, the model fit analysis of the determinants of well-being is explored.The paper reports that students' classroom engagement does mediate in the path of academic grit and well-being, and in the path of intolerance to uncertainty and well-being. It also evidence the model fit of the influence of the determinants of well-being on that of school students during the second wave of the COVID-19 pandemic. The study also draws implications and suggestions for educators using the current model of students' well-being. 2022 Wiley Periodicals LLC. -
Growth trajectories for executive and social cognitive abilities in an Indian population sample: Impact of demographic and psychosocial determinants
Cognitive abilities are markers of brain development and psychopathology. Abilities, across executive, and social domains need better characterization over development, including factors that influence developmental change. This study is based on the cVEDA [Consortium on Vulnerability to Externalizing Disorders and Addictions] study, an Indian population based developmental cohort. Verbal working memory, visuo-spatial working memory, response inhibition, set-shifting, and social cognition (faux pas recognition and emotion recognition) were cross-sectionally assessed in > 8000 individuals over the ages 623 years. There was adequate representation across sex, urban-rural background, psychosocial risk (psychopathology, childhood adversity and wealth index, i.e. socio-economic status). Quantile regression was used to model developmental change. Age-based trajectories were generated, along with examination of the impact of determinants (sex, childhood adversity, and wealth index). Development in both executive and social cognitive abilities continued into adulthood. Maturation and stabilization occurred in increasing order of complexity, from working memory to inhibitory control to cognitive flexibility. Age related change was more pronounced for low quantiles in response inhibition (??4 versus =2 for higher quantiles), but for higher quantiles in set-shifting (? > ?1 versus ?0.25 for lower quantiles). Wealth index had the largest influence on developmental change across cognitive abilities. Sex differences were prominent in response inhibition, set-shifting and emotion recognition. Childhood adversity had a negative influence on cognitive development. These findings add to the limited literature on patterns and determinants of cognitive development. They have implications for understanding developmental vulnerabilities in young persons, and the need for providing conducive socio-economic environments. 2023 Elsevier B.V. -
Heterojunction engineered MWCNT/Ag3PO4 via organic acid and its natural light-assisted photocatalytic efficiency
Compositing photoactive, but unstable semiconductors with low dimensional carbon-based materials and modulating the hetero junction between them can assure more efficient and stable systems for the remediation of severe pollutants. The current study has given emphasis to understand the role of sulfonic acid in making a compact heterojunction between AP and MWCNTs, considering the effective delocalization of carriers and the direct relationship with the photoactivity. The significant reduction in the band gap of AP from 2.320 to 2.0516ev after the introduction of MWCNTs unmistakably confirmed the compatibility between the composite moieties. The intensity of the photoluminescence peak observed at an emission wavelength of 350nm for pure AP was found to be minimized in the composite, which confirms the effective charge delocalization from AP to the conductive MWCNTs. The closest bond distance was observed in the range of 2.3 to 2.5between an O atom of Ag3PO4 and a C atom of CNT, which explains the tight contact between the species. The photoactivity studies unambiguously confirmed the potential of the organic acid at the composite interface as it could accomplish 99% dye degradation within a span of 8min, whilst the system without the organic acid exhibited complete degradation within a span of 60min. The p-XRD analysis of the catalyst recovered from the reaction mixture revealed its high stability. 2023 Elsevier B.V. -
Bioengineering of biowaste to recover bioproducts and bioenergy: A circular economy approach towards sustainable zero-waste environment
The inevitable need for waste valorisation and management has revolutionized the way in which the waste is visualised as a potential biorefinery for various product development rather than offensive trash. Biowaste has emerged as a potential feedstock to produce several value-added products. Bioenergy generation is one of the potential applications originating from the valorisation of biowaste. Bioenergy production requires analysis and optimization of various parameters such as biowaste composition and conversion potential to develop innovative and sustainable technologies for most effective utilization of biowaste with enhanced bioenergy production. In this context, feedstocks, such as food, agriculture, beverage, and municipal solid waste act as promising resources to produce renewable energy. Similarly, the concept of microbial fuel cells employing biowaste has clearly gained research focus in the past few decades. Despite of these potential benefits, the area of bioenergy generation still is in infancy and requires more interdisciplinary research to be sustainable alternatives. This review is aimed at analysing the bioconversion potential of biowaste to renewable energy. The possibility of valorising underutilized biowaste substrates is elaborately presented. In addition, the application and efficiency of microbial fuel cells in utilizing biowaste are described in detail taking into consideration of its great scope. Furthermore, the review addresses the significance bioreactor development for energy production along with major challenges and future prospects in bioenergy production. Based on this review it can be concluded that bioenergy production utilizing biowaste can clearly open new avenues in the field of waste valorisation and energy research. Systematic and strategic developments considering the techno economic feasibilities of this excellent energy generation process will make them a true sustainable alternative for conventional energy sources. 2023 Elsevier Ltd -
Soft excess in AGN with relativistic X-ray reflection
The soft X-ray excess, emission below (Formula presented.) 2keV over the X-ray power-law, is a marked spectral component in the X-ray spectra of many Seyfert1 galaxies. We investigate if the observed soft X-ray excess in a sample of Seyfert1s is in accordance with the prediction of the relativistic reflection model by analyzing the XMM-Newton and NuSTAR spectra. The fractional difference in the soft excess (SE) obtained from the blurred reflection emission predicted (from NuSTAR) and the observed (from XMM-Newton) luminosities show that the reflection model underestimates the SE emission in our sample. The results point to alternative models (for example, warm Comptonization) to explain the soft X-ray excess in AGN. 2023 Wiley-VCH GmbH. -
Mechanical Strength and Microstructure of GGBS-SCBA based Geopolymer Concrete
This paper deals with the attempt to develop and study the performance of ground granulated blast furnace slag (GGBS) and sugarcane bagasse ash (SCBA) based sustainable geopolymer concrete. NaOH (8M, 10M, and 12M) and Na2SiO3 were used as alkaline activators with a ratio of 2.5. SCBA mainly acted as amorphous silica and has been utilized as a substitute material for GGBS. The effect of SCBA contents (0%, 5%, 10%, 15% & 20% by the mass of binder) in terms of fresh, hardened, microstructural, and correlation properties of geopolymer concrete developed have been evaluated. Different tests such as the slump cone test, compression test, split tensile test, flexure test, and ultrasonic pulse velocity test were conducted. Scanning electron microscopy, Energy dispersive analysis, and X-ray diffraction analysis were investigated for understanding the microstructural properties. The research findings have shown that with an increase in molarity from 8M to 12M there is an increase in the strength properties of geopolymer concrete. The results in this current study show that 28 days compressive strength was found to increase by 415% when the NaOH molarity was increased from 8M to 10M and 821% when the NaOH molarity was increased from 8M to 12M. The geopolymer concrete developed with 20% SCBA and 80% GGBS with 8M NaOH solution and SS/SH ratio of 2.5 can be used for a target strength of 3035 MPa. Scanning electron microscope images show a packed and dense matrix, which clearly outlines the reason behind the attainment of higher strength in higher molarity of GGBS-SCBA based geopolymer concrete samples and the presence of CASH gel confirmed this in the geopolymer matrix. Furthermore, there is a strong correlation between the experimental findings and the model equations proposed. These presented models will be useful in improving the strength of geopolymer concrete incorporating agricultural and industrial wastes. 2023 The Authors -
Does Google Trend Affect Cryptocurrency? An Application of Panel Data Approach
Cryptocurrency has emerged globally as the most profitable investment asset of the decade. The media exposure and reportage on cryptocurrency are frequent, and it seems that prices of cryptocurrencies could only rise higher. In today's digital world, any individual's first go-to information-seeking platform is the Google search engine. Thus, it is imperative to understand how Google's search trend affects an investable asset and its market as a whole. Researchers have explored varied sentiment measurement proxies such as news coverage, Facebook and Twitter posts, and, most importantly, Google searches. Numerous research studies show increasing interest in Google search volume and its predictive ability to understand investment returns and economic outcomes. In a behavioural finance context, the present research uses Pearson's correlation and panel regression to examine the association of cryptocurrency returns (Bitcoin, Ethereum, and Ripple) and their varied characteristics with the Google search intensity. The study's findings reveal that investors searching for information on Cryptocurrency online drive the price increase in cryptocurrency and push the trading volume up and increase the volatility of the cryptocurrency returns. Furthermore, investor sentiment has a statistically significant impact on cryptocurrencies' trading volume and weekly volatility in periods of high or greedy investor sentiment. The findings imply that the 'price pressure hypothesis' given by Barber and Odean (2008) as a stock market research finding is also present in the cryptocurrency market. 2023 SCMS Group of Educational Institutions. All rights reserved. -
EPCAEnhanced Principal Component Analysis for Medical Data Dimensionality Reduction
Innovations in technology from thelast one decade have led to the generation of colossal amounts of medical data with comparably low cost. Medical data should be collected with utmost care. Sometimes, the data have high features but not all the features play an important role in drawing the relations to the mining task. For the training of machine learning algorithms, all the attributes in the data set are not relevant. Some of the characteristics may be negligible and some characteristics may not influence the outcome of the forecast. The pressure on machine learning algorithms can be minimized by ignoring or taking out the irrelevant attributes. Reducing the attributes must be done at the risk of information loss. In this research work, an Enhanced Principal Component Analysis (EPCA) is proposed, which reduces the dimensions of the medical dataset and takes paramount care of not losing important information, thereby achieving good and enhanced outcomes. The prominent dimensionality reduction techniques such as Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Partial Least Squares (PLS), Random Forest, Logistic Regression, Decision Tree and the proposed EPCA are investigated on the following Machine Learning (ML) algorithms: Support Vector Machine (SVM), Artificial Neural Networks (ANN), Nae Bayes (NB) and Ensemble ANN (EANN) using statistical metrics such as F1 score, precision, accuracy and recall. To optimize the distribution of the data in the low-dimensional representation, EPCA directly mapped the data to a space with fewer dimensions. This is a result of feature correlation, which made it easier to recognize patterns. Additionally, because the dataset under consideration was multicollinear, EPCA aided in speeding computation by lowering the data's dimensionality and therebyenhancedthe classification model's accuracy. Due to these reasons, the experimental results showed that the proposed EPCA dimensionality reduction technique performed better when compared with other models. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Development and Validation of the Multidimensional Psychosocial Risk Screen (MPRS): An Approach towards Primary Prevention
Background: The prevalence of mental health problems in adolescents has been identified as a global concern. Early screening and identification can offer benefits in terms of primary prevention and reduced healthcare costs. This study aimed to develop a tool to assess the risk of developing mental health problems in adolescents. Methods: The study followed an exploratory sequential design and was divided into five phases. The Multidimensional Psychosocial Risk Screen (MPRS) is a newly developed self-report measure. The various steps in its development and validation have been elaborated. The MPRS was evaluated with a sample of 934 adolescents aged 12-18, spread across the 8th-12th grade. Results: Exploratory and confirmatory factor analyses revealed a robust factor structure. The extracted five factors were named as Parent-Child Relationship (PCR), Self-Concept (SC), Teacher-Student Dynamics (TSD), Social Media Use (SMU), and Peer Interaction (PI). The reliability of the subscales ranged from 0.60 to 0.80. The overall reliability of the scale was good (a = 0.87). Convergent validity of the scale was established using standard measures of risk factors and emotional and behavioural problems. Conclusions: The MPRS can be considered an effective tool with an adequate factor structure and good psychometric properties. It can be beneficial in the early detection of vulnerabilities to mental health problems in adolescents and, therefore, seen as a key element in primary prevention and fostering individualized interventions. 2023 The Author(s). -
pH-dependent water permeability switching and its memory in MoS2 membranes
Intelligent transport of molecular species across different barriers is critical for various biological functions and is achieved through the unique properties of biological membranes14. Two essential features of intelligent transport are the ability to (1) adapt to different external and internal conditions and (2) memorize the previous state5. In biological systems, the most common form of such intelligence is expressed as hysteresis6. Despite numerous advances made over previous decades on smart membranes, it remains a challenge to create a synthetic membrane with stable hysteretic behaviour for molecular transport711. Here we demonstrate the memory effects and stimuli-regulated transport of molecules through an intelligent, phase-changing MoS2 membrane in response to external pH. We show that water and ion permeation through 1T? MoS2 membranes follows a pH-dependent hysteresis with a permeation rate that switches by a few orders of magnitude. We establish that this phenomenon is unique to the 1T? phase of MoS2, due to the presence of surface charge and exchangeable ions on the surface. We further demonstrate the potential application of this phenomenon in autonomous wound infection monitoring and pH-dependent nanofiltration. Our work deepens understanding of the mechanism of water transport at the nanoscale and opens an avenue for the development of intelligent membranes. 2023, The Author(s), under exclusive licence to Springer Nature Limited. -
Big data-Industry 4.0 readiness factors for sustainable supply chain management: Towards circularity
Big data-Industry 4.0 interaction is expected to revolutionize the existing supply chains in recent years. While increased operational efficiency and enhanced decision-making are the primary advantages studied widely, the sustainable aspects of digital supply chain in the circular economy era have received limited attention. The previous literature rarely explores the industry readiness for a digital supply chain. Thus, the present study objectives to explore Big data-Industry 4.0 readiness factors for sustainable supply chain management. A detailed literature analysis was performed to identify a total of seventeen readiness factors for sustainable supply chain management. A team of six experts were consulted to perform the pairwise comparison for the identified potential readiness factors. This study adopts a fuzzy best-worst method to prioritize the readiness factors according to their degree of influence. The results from the study reflect that readiness towards information system infrastructure, Internet stability for developing I4.0 infrastructure, and circular process and awareness are the most significant readiness factors. The potential recommendation of this study includes the increased attention from sustainable supply chain stakeholders on developing infrastructure, including knowledge building exercise and training process focused on circular economy process. The findings from the study will assist sustainable supply chain stakeholders to frame strategies and action plans during the digitalization of supply chains. 2023 Elsevier Ltd -
Memetic Spider Monkey Optimization for Spam Review Detection Problem
Spider monkey optimization (SMO) algorithm imitates the spider monkey's fission-fusion social behavior. It is evident through literature that the SMO is a competitive swarm-based algorithm that is used to solve difficult real-life problems. The SMO's search process is a little bit biased by the random component that drives it with high explorative searching steps. A hybridized SMO with a memetic search to improve the local search ability of SMO is proposed here. The newly developed strategy is titled Memetic SMO (MeSMO). Further, the proposed MeSMO-based clustering approach is applied to solve a big data problem, namely, the spam review detection problem. A customer usually makes decisions to purchase something or make an image of someone based on online reviews. Therefore, there is a good chance that the individuals or companies may write spam reviews to upgrade or degrade the stature or value of a trader/product/company. Therefore, an efficient spam detection algorithm, MeSMO, is proposed and tested over four complex spam datasets. The reported results of MeSMO are compared with the outcomes obtained from the six state-of-art strategies. A comparative analysis of the results proved that MeSMO is a good technique to solve the spam review detection problem and improved precision by 3.68%. 2023 Mary Ann Liebert, Inc., publishers. -
Minimizing Energy Depletion Using Extended Lifespan: QoS Satisfied Multiple Learned Rate (ELQSSM-ML) for Increased Lifespan of Mobile Adhoc Networks (MANET)
Mobile Adhoc Networks (MANETs) typically employ with the aid of new technology to increase Quality-of-Service (QoS) when forwarding multiple data rates. This kind of network causes high forwarding delays and improper data transfer rates because of the changes in the nodes vicinity. Although an optimized routing technique to transfer energy has been used to lessen the delay and improve the throughput by assigning a proper data rate, it does not consider the objective of minimizing the energy use, which results in less network lifetime. The goal of the proposed work is to minimize the energy depletion in a MANET, which results in an extended Lifespan of the network. In this research paper, an Extended Life span and QSSM-ML routing algorithm is proposed, which minimizes energy use and enhances the network lifetime. First, an optimization problem is formulated with the purpose of increasing the networks lifetime while limiting the energy utilization and stability of the path along with residual. Second, an adaptive policy is applied for the asymmetric distribution of energy at both origin and intermediate nodes. In order to achieve maximum network lifespan and minimal energy depletion, the optimization problem was framed when power usage is a constraint by allowing the network to make use of the leftover power. An asymmetric energy transmission strategy was also designed for the adaptive allocation of maximum transmission energy in the origin. This made the network lifespan extended with the help of reducing the nodes energy use for broadcasting the data from the origin to the target. Moreover, the nodes energy use during packet forwarding is reduced to recover the network lifetime. The overall benefit of the proposed work is that it can achieve both minimal energy depletion and maximizes the lifetime of the network. Finally, the simulation findings reveal that the ELQSSM-ML algorithm accomplishes a better network performance than the classical algorithms. 2023 by the authors. -
Does Credit Rating Revisions Affect the Price of Common Stock: A Study of Indian Capital Market
The current investigation aims to assess the effect of credit assessment changes on the share prices of Indian companies from 2009 to 2019. The data of top 100 companies listed on National Stock Exchange (NSE) across 10 industries stem from CMIE databases. The excess stock return is compared with the market in a 15-day window around credit rating changes. The event effect on share prices is more in the pre-event window compared to the post-event window. Positive abnormal stock returns around upgrades through downgrades are statistically significant compared to upgrades. Credit ratings are not significant across industries, and agency nationality is a critical factor for calculating the intensity of price reaction. 2021 K. J. Somaiya Institute of Management.